Simultaneous estimation of the heat of reaction and the heat transfer coefficient by calorimetry: estimation problems due to model simplification and high jacket flow rates—theoretical development

Application of heat compensation calorimetry to an E. coli fed-batch process

The application of biocalorimetry to fermentation processes offers advantageous insights, while being less complex compared to other, sophisticated PAT solutions. Although the general concept is established, calorimetric methods vary in detail. In this work, a special approach, called heat compensation calorimetry, was applied to an E. coli fed-batch process. Much work has been done for batch processes, proving the validity and accuracy of this calorimetric mode. However, the adaption of this strategy to fed-batch processes has some implications. In the first section of this work, batch fermentations were performed, comparing heat capacity calorimetry to the compensation mode. Both processes showed very good agreement by means of growth behavior. The heat related differences, e.g. temperature profiles, were obvious. In addition, the impact of the chosen mode on the calculation of in-process heat transfer coefficients was shown. Finally, a fed-batch fermentation was performed. The compensation mode was kept sufficiently, up to the point where the metabolic heat production accelerated strongly. Controller tuning was a neuralgic point, which would have needed further optimization under these conditions. Nevertheless, in the present work it was possible to realize a working compensation process while demonstrating critical aspects that must be considered when establishing such approach.

Novel Formulation of Adaptive MPC as EKF Using ANN Model: Multiproduct Semibatch Polymerization Reactor Case Study

In this paper, a novel formulation for nonlinear model predictive control (MPC) has been proposed incorporating the extended Kalman filter (EKF) control concept using a purely data-driven artificial neural network (ANN) model based on measurements for supervisory control. The proposed scheme consists of two modules focusing on online parameter estimation based on past measurements and control estimation over control horizon based on minimizing the deviation of model output predictions from set points along the prediction horizon. An industrial case study for temperature control of a multiproduct semibatch polymerization reactor posed as a challenge problem has been considered as a test bed to apply the proposed ANN-EKFMPC strategy at supervisory level as a cascade control configuration along with proportional integral controller [ANN-EKFMPC with PI (ANN-EKFMPC-PI)]. The proposed approach is formulated incorporating all aspects of MPC including move suppression factor for control effort minimization and constraint-handling capability including terminal constraints. The nominal stability analysis and offset-free tracking capabilities of the proposed controller are proved. Its performance is evaluated by comparison with a standard MPC-based cascade control approach using the same adaptive ANN model. The ANN-EKFMPC-PI control configuration has shown better controller performance in terms of temperature tracking, smoother input profiles, as well as constraint-handling ability compared with the ANN-MPC with PI approach for two products in summer and winter. The proposed scheme is found to be versatile although it is based on a purely data-driven model with online parameter estimation.